PM 566 Midterm - California Supplier Diversity and Net Income

Author

Norma Marshall

library(dplyr)
library(readxl)
library(tidygeocoder)
library(leaflet)
library(tidyr)
library(knitr)
library(ggplot2)
library(htmltools)
library(tidytext)
library(readr)
library(stringr)    
library(forcats)  
library (plotly)
library(writexl)

Background and Research Question

To accelerate efforts to reduce health care disparities, hospitals and health systems increase their efforts in core areas such as staff and leadership diversity and cultural competence. However, the economic relationship between healthcare systems and the communities they serve are growing in importance.

Supplier diversity refers to when an organization procures goods and services from a variety of businesses, including those that are at least 51% owned, managed, and operated from marginalized and minority groups. These include women, veterans, African Americans, LGBTQIA+, and more. The private sector, including the healthcare industry has incorporated supplier diversity programs into their business practices after historically being adopted by the federal government and its contractors. According to the Harvard Business Review, supplier diversity programs are important in combatting social injustice and systemic racism in the US as they actively include diverse representation and inclusion in hospital operations and supply chains. In addition, for the moral and ethical arguments, supplier diversity programs have commercial in hospitals and health systems. These benefits include greater innovation and value through cost reductions, expansions of external partnerships, local job creation, better understanding of supply chain sourcing process and sources, and easier compliance with government and grant contracts.

On an annual basis (individual hospital fiscal year), individual hospitals and systems report detailed facility level financial data to the Department of Health Care Access and Information (HCAI). This data includes detailed facility level data on services capacity, inpatient/outpatient utilization, revenues, and expenses by type and payer. In addition, the Health and Safety Code Section 1339.85-1339.87 requires individual hospitals with operating expenses over $50 million to report hospital supplier and diversity reports explaining the hospitals’ supplier diversity statement and procurement efforts regarding minority, women, LGBT, and disabled veteran enterprises.

This report merges the annual financial data and supplier diversity reports for 2023 to answer the question Are California hospitals with diverse suppliers profitable? Supplier diversity aims to increase innovation and drive down prices for supplies and goods though competition while also aiming to improve health equity and combat social injustice in the US through business practices. This exploratory data analysis aims to see if funds dedicated to goods and services from diversely owned businesses can lead to better financial outcomes.

Methods

A novel dataset was collected by merging two data sets, HCAI’s Hospital Annual Financial Disclosure Report for 2023, and HCAI’s Supplier Diversity report from 2023. Required by state law, supplier diversity and financial data are reported each year to the HCAI. Datasets were merged on the shared hospital name variable to create a comprehensive data containing both supplier diversity and financial metrics for each hospital.

Following the merge, several variables indicating procurement from diverse backgrounds were recoded to be used as numeric variables, allowing for quantitative analysis. Address related variables were recoded into latitude and longitude variables to facilitate geocoding using the tidygeocoder package for recoding and the leaflet package for visualization.

Frequency tables were generated to view top hospitals in net income, supplier procurement from different backgrounds, and demographic data. Maps were generated to locate the top performing hospitals and correlations were run to quantify the relationship between supplier procurement and hospital net income.

hospital_suppliers <- read_excel("supplier-diversity-report-2023-extract-.xlsx")
hospital_finances <- read_excel("hadr-2023.xlsx")
hospitals <- merge(hospital_suppliers, hospital_finances, by = "Hospital_Name", all.x = TRUE)
final_hospitals <- hospitals %>%
  select(Hospital_Name, Hospital_Address, Type_Control, County, MSSA, Supplier_Diversity_Statement, Encourage_Suppliers, Encourage_Employees, Conduct_Outreach_Comm, Certification_Support, Tier_I_African_American, Tier_II_African_American, Total_African_American, Tier_I_Hispanic_American, Tier_II_Hispanic_American, Total_Hispanic_American, Tier_I_Native_American, Tier_II_Native_American, Total_Native_American, Tier_I_Asian_Pacific_American, Tier_II_Asian_Pacific_American, Total_Asian_Pacific_American, Tier_I_Unknown_Minority, Tier_II_Unknown_Minority, Total_Unknown_Minority, Total_Tier_I_Minority, Total_Tier_II_Minority, Total_Minority, Tier_I_Women, Tier_II_Women, Total_Women, Tier_I_LGBT, Tier_II_LGBT, Total_LGBT, Tier_I_Disabled_Veteran, Tier_II_Disabled_Veteran, Total_Disabled_Veteran, Tier_I_Less_Duplicated_Amount, Tier_II_Less_Duplicated_Amount, Total_Less_Duplicated_Amount, Combined_Tier_I_Total, Combined_Tier_II_Total, Combined_Total, Total_Hospital_Procurement, ADDRESS, CITY, ZIP_CODE, GR_PT_REV, DED_FR_REV, TOT_CAP_REV, NET_PT_REV, OTH_OP_REV, TOT_OP_EXP, NET_FRM_OP, NONOP_REV, NONOP_EXP, INC_TAX, EXT_ITEM, NET_INCOME, EXP_SAL, EXP_BEN, EXP_PHYS, EXP_OTHPRO, EXP_SUPP, EXP_PURCH, EXP_DEPRE, EXP_LEASES, EXP_INSUR, EXP_INTRST, EXP_OTH
)  
final_hospitals <- final_hospitals %>%
  unite("full_address", ADDRESS, CITY, ZIP_CODE, sep = ", ", remove = FALSE) %>%
  geocode(address = full_address, method = "osm", lat = latitude, long = longitude)
final_hospitals <- final_hospitals %>%
  mutate(
    Combined_Total = as.numeric(as.character(Combined_Total)),  
    Total_Hospital_Procurement = as.numeric(as.character(Total_Hospital_Procurement)), 
    Total_Minority = as.numeric(as.character(Total_Minority))  
  )

final_hospitals <- final_hospitals %>%
  mutate(across(c(Tier_I_African_American, Tier_II_African_American, Total_African_American,
                  Tier_I_Hispanic_American, Tier_II_Hispanic_American, Total_Hispanic_American,
                  Tier_I_Native_American, Tier_II_Native_American, Total_Native_American,
                  Tier_I_Asian_Pacific_American, Tier_II_Asian_Pacific_American, Total_Asian_Pacific_American,
                  Tier_I_Unknown_Minority, Tier_II_Unknown_Minority, Total_Unknown_Minority,
                  Total_Tier_I_Minority, Total_Tier_II_Minority, Total_Minority,
                  Tier_I_Women, Tier_II_Women, Total_Women,
                  Tier_I_LGBT, Tier_II_LGBT, Total_LGBT,
                  Tier_I_Disabled_Veteran, Tier_II_Disabled_Veteran, Total_Disabled_Veteran,
                  Tier_I_Less_Duplicated_Amount, Tier_II_Less_Duplicated_Amount, Total_Less_Duplicated_Amount,
                  Combined_Tier_I_Total, Combined_Tier_II_Total, Combined_Total, Total_Hospital_Procurement),
                as.numeric))

Demographics

In this report there are 372 hospitals with both financial and supplier diversity data reported the to the HCAI in 2023. On average these hospitals report earning over 24 million dollars over the course of the year and spend over $9.6 million on supplies from diverse suppliers, totaling about 9 percent of the total dollars spent on the procurement on supplies annually.

A majority of these hospitals (320) serve urban areas in California, as demonstrated by large clusters of hospitals around major cities such as San Francisco, San Diego, and Los Angeles. Over 53% of the hospitals are nonprofit, including church related facilities.

leaflet(data = final_hospitals) %>%
  addTiles() %>%
  addCircleMarkers(~longitude, ~latitude,
                   popup = ~Hospital_Name,  # Display hospital name on click
                   radius = 5, color = "blue", fill = TRUE, fillOpacity = 0.7) %>%
  setView(lng = -119.4179, lat = 36.7783, zoom = 6)  # Center on California
summary_table <- final_hospitals %>%
  summarize(
    `Total Hospitals` = n(),
    `Average Procurement from Diverse Suppliers` = mean(Combined_Total, na.rm = TRUE),
    `Average Total Hospital Procurement` = mean(Total_Hospital_Procurement, na.rm = TRUE),
    `Average Net Income` = mean(NET_INCOME, na.rm = TRUE)
  )

kable(summary_table, caption = "Summary Table of Hospital Data")
Summary Table of Hospital Data
Total Hospitals Average Procurement from Diverse Suppliers Average Total Hospital Procurement Average Net Income
372 9577526 115754584 24413739
urban_rural_freq <- final_hospitals %>%
  group_by(MSSA) %>%
  summarize(Frequency = n()) %>%
  ungroup()

type_control_freq <- final_hospitals %>%
  group_by(Type_Control) %>%
  summarize(Frequency = n()) %>%
  ungroup()

print(kable(urban_rural_freq, caption = "Frequencies of Urban/Rural Hospitals"))


Table: Frequencies of Urban/Rural Hospitals

|MSSA  | Frequency|
|:-----|---------:|
|Rural |        52|
|Urban |       320|
print(kable(type_control_freq, caption = "Frequencies of Hospital Types (Type_Control)"))


Table: Frequencies of Hospital Types (Type_Control)

|Type_Control                                  | Frequency|
|:---------------------------------------------|---------:|
|City or County                                |        23|
|District                                      |        17|
|Investor - Corporation                        |        50|
|Investor - Limited Liability Company          |        57|
|Investor - Partnership                        |        11|
|Non-profit Corporation (incl. Church-related) |       198|
|State                                         |         6|
|University of California                      |        10|

How much are hosptials spending on supplies from minority owned businessess?

Washington Hospital in Fremont spends in the most on minority owned suppliers, followed by Stanford Health Care. In specific category, Kaiser Permanente in Santa Clara spends the most on African American suppliers while Stanford leads for Hispanic and Asian/Pacific Category. Washington Hospital in Fremont also is the top performer in the unknown minority category while, such as Kaiser Foundation Hospital - San Diego and Contra Costa Regional Medical Center, lead in categories like total women-owned and LGBT-owned suppliers, respectively.

# Get the top diverse hospitals and select only the relevant columns
top_diverse_hospitals <- final_hospitals %>%
  arrange(desc(Total_Minority)) %>%  
  select(Hospital_Name, Total_Minority) %>%  
  head(n = 10)  

kable(top_diverse_hospitals, col.names = c("Hospital Name", "Combined Total Spent on Minority Owned Suppliers"))
Hospital Name Combined Total Spent on Minority Owned Suppliers
WASHINGTON HOSPITAL - FREMONT 265276375
STANFORD HEALTH CARE 113963711
CHILDREN’S HOSPITAL OF ORANGE COUNTY 66667480
UCSF MEDICAL CENTER 54596986
KAISER FOUNDATION HOSPITAL - SAN DIEGO - CLAIREMONT MESA 50859953
KAISER FOUNDATION HOSPITAL - DOWNEY 46252720
KAISER FOUNDATION HOSPITAL - SANTA CLARA 45552873
KAISER FOUNDATION HOSPITAL - RIVERSIDE 38359794
KAISER FOUNDATION HOSPITAL - LOS ANGELES 37618486
CEDARS-SINAI MEDICAL CENTER 35384829
selected_vars <- c(
  "Total_African_American", "Total_Hispanic_American",
  "Total_Native_American", "Total_Asian_Pacific_American",
  "Total_Unknown_Minority", "Total_Minority",
  "Total_Women", "Total_LGBT", "Total_Disabled_Veteran"
)

top_hospitals_df <- data.frame(Top_Hospital = character(), Top_Value = numeric(), Category = character(), stringsAsFactors = FALSE)

# Loop through each variable to find the top hospital
for (var_name in selected_vars) {
  top_hospital <- final_hospitals %>%
    filter(!is.na(!!sym(var_name))) %>%  # Exclude NA values for the variable
    top_n(1, !!sym(var_name)) %>%       # Get the hospital with the highest value
    select(Hospital_Name, !!sym(var_name)) %>%  # Select relevant columns
    rename(Top_Hospital = Hospital_Name, Top_Value = !!sym(var_name)) %>%  # Rename for clarity
    mutate(Category = var_name)  # Add the category as a new column
  
  top_hospitals_df <- rbind(top_hospitals_df, top_hospital)
}

# Display the table with kable, including the Category column
kable(top_hospitals_df, 
      col.names = c("Top Hospital", "Top Value", "Category"),
      caption = "Top Performing Hospitals by Category")
Top Performing Hospitals by Category
Top Hospital Top Value Category
KAISER FOUNDATION HOSPITAL - SANTA CLARA 16711025 Total_African_American
STANFORD HEALTH CARE 18140655 Total_Hispanic_American
KECK HOSPITAL OF USC 1649217 Total_Native_American
STANFORD HEALTH CARE 86407777 Total_Asian_Pacific_American
WASHINGTON HOSPITAL - FREMONT 252433738 Total_Unknown_Minority
WASHINGTON HOSPITAL - FREMONT 265276375 Total_Minority
KAISER FOUNDATION HOSPITAL - SAN DIEGO - CLAIREMONT MESA 33411813 Total_Women
CONTRA COSTA REGIONAL MEDICAL CENTER 30308261 Total_LGBT
KAISER FOUNDATION HOSPITAL - MODESTO 17341068 Total_Disabled_Veteran

Where are the most diverse hopstials located?

Across categories and in total, a majority of hospitals that spend the most money on diverse suppliers residei n the bay area. However, there are a signicant number of hospitals that spend a lot of money suppliers from women and disabled veterans in Southern California. Hospitals in northern California include a few state and University of California hospitals including UC San Francisco and Stanford.

total_minority_palette <- colorNumeric(palette = "Reds", 
                                       domain = final_hospitals$Total_Minority)

# Create a leaflet plot for hospitals
leafplot <- leaflet(final_hospitals) %>% 
  addProviderTiles('CartoDB.Positron') %>% 
  addCircles(
    lat = ~latitude,  
    lng = ~longitude,  
    label = ~paste0("Hospital: ", Hospital_Name, "<br>Total Minority: ", Total_Minority),  
    color = ~total_minority_palette(Total_Minority),  
    opacity = 1, 
    fillOpacity = 1,
    stroke = FALSE,  
    radius = 5       
  ) %>%
  addLegend('bottomleft', 
            pal = total_minority_palette, 
            values = final_hospitals$Total_Minority,
            title = 'Total Minority in Hospitals', 
            opacity = 1) %>%
  setView(lng = -119.4179, lat = 36.7783, zoom = 6)  # Set view to California


leafplot
# Explicitly list the variables to create maps for
selected_vars <- c(
  "Total_African_American", "Total_Hispanic_American",
  "Total_Native_American", "Total_Asian_Pacific_American",
  "Total_Unknown_Minority", "Total_Minority",
  "Total_Women", "Total_LGBT", "Total_Disabled_Veteran"
)

create_map <- function(var_name) {
  color_pal <- colorNumeric("Purples", domain = final_hospitals[[var_name]], na.color = "transparent")
  
  leaflet(data = final_hospitals) %>%
    addProviderTiles("CartoDB.Positron") %>%
    setView(lng = -119.4179, lat = 36.7783, zoom = 6) %>%  
    addCircles(
      lat = ~latitude, lng = ~longitude,
      color = ~color_pal(final_hospitals[[var_name]]),  
      opacity = 1, fillOpacity = 0.8
    ) %>%
    addLegend("bottomleft", pal = color_pal, values = final_hospitals[[var_name]],
              title = var_name, opacity = 1)
}

# Generate a list of maps for each specified variable
map_list <- lapply(selected_vars, create_map)

# Display all maps in one view
browsable(tagList(map_list))

What hosptials make the most money?

Regardless of supplier diversity, the top earning hosptials are in the bay area or Los Angeles/Orange County areas. Cedars_Sinai, LA General, UCLA, and Childrens Hopsital are in the top 10 earning hospitals in California, however this does not mean they spend the most on supplies from minority owned businesses.

Most hospitals in the plot below net the average amount in income every year, with some like Eden Medical Center earning the least amount of income.

# Load the knitr package if it’s not already loaded
library(knitr)

# Prepare the top hospitals table
top_hospitals <- final_hospitals %>%
  arrange(desc(NET_INCOME)) %>%  
  head(n = 10) %>%  
  select(Hospital_Name, NET_INCOME)

# Display the table with just kable
kable(top_hospitals, 
      col.names = c("Hospital Name", "Net Income"), 
      caption = "Top 10 Hospitals by Net Income",
      format = "html")  # Specify HTML format to ensure compatibility in an HTML document
Top 10 Hospitals by Net Income
Hospital Name Net Income
STANFORD HEALTH CARE 808452386
CEDARS-SINAI MEDICAL CENTER 570706272
RADY CHILDREN'S HOSPITAL - SAN DIEGO 522677659
LOS ANGELES GENERAL MEDICAL CENTER 475854337
EL CAMINO HEALTH 315951240
RONALD REAGAN UCLA MEDICAL CENTER 303667992
HOAG MEMORIAL HOSPITAL PRESBYTERIAN 302767652
SHARP MEMORIAL HOSPITAL 298370158
CHILDREN'S HOSPITAL OF ORANGE COUNTY 234476380
KAISER FOUNDATION HOSPITAL - SANTA CLARA 231096595
library(plotly)

# Create a scatter plot for NET_INCOME across all hospitals
net_income_scatter_plot <- ggplot(final_hospitals, aes(x = seq_along(NET_INCOME), y = NET_INCOME, text = Hospital_Name)) +
  geom_point(color = "blue", alpha = 0.6) +  
  theme_minimal() +
  labs(
    title = "Scatter Plot of Net Income Across All Hospitals",
    x = "Hospital Index",  # X-axis label indicating each hospital's position
    y = "Net Income"
  )

# Convert the ggplot to an interactive plotly plot
interactive_plot <- ggplotly(net_income_scatter_plot, tooltip = "text")

interactive_plot

Is diverse supplier procurement correlated with net hospital income?

Each hospital also reports tier 1 and tier 2 spend for each category of diverse suppliers. Tier 1 spend refers to the total amount of procurement dollars that an organization spends directly with a certified diverse supplier. Tier 2, on the other hand represents the total amount of procurement dollars that an organization spends with a given supplier, who then sources products and services from another certified diverse supplier. Tier 2 relationships enable hospitals to increase diversity spend while working with existing suppliers or with suppliers who may possess rare capabilities.

To investigate whether or not the relationship between supplier diversity and net income for a hospital can vary between these two types of procurement from diverse suppliers. Correlations were ran between net income and supplier procurement.

There is moderate positive correlation between Tier 1 suppliers that are Hispanic, and women owned (0.59,0.60), as well a s total procurement from Hispanic owned suppliers. There is also a slight negative correlation between tier two supplier procurement from suppliers that are women owned, LGBT owned, and overall (<0) suggesting a negative correlation between increasing tier 2 procurement form diverse suppliers and net income.

selected_vars <- c(
  "Total_African_American", "Total_Hispanic_American",
  "Total_Native_American", "Total_Asian_Pacific_American",
  "Total_Unknown_Minority", "Total_Minority",
  "Total_Women", "Total_LGBT", "Total_Disabled_Veteran",
  "NET_INCOME"
)

correlation_data <- final_hospitals %>%
  select(all_of(selected_vars)) %>%
  filter(!is.na(NET_INCOME))  # Exclude rows with NA in NET_INCOME

correlation_matrix <- cor(correlation_data, use = "complete.obs")

cor_with_net_income_overall <- correlation_matrix["NET_INCOME", selected_vars[-length(selected_vars)]]

# Define Tier I and Tier II variables
tier1_vars <- c(
  "Tier_I_African_American",
  "Tier_I_Hispanic_American",
  "Tier_I_Native_American",
  "Tier_I_Asian_Pacific_American",
  "Tier_I_Unknown_Minority",
  "Total_Tier_I_Minority",
  "Tier_I_Women",
  "Tier_I_LGBT",
  "Tier_I_Disabled_Veteran",
  "Tier_I_Less_Duplicated_Amount",
  "Combined_Tier_I_Total"
)

tier2_vars <- c(
  "Tier_II_African_American",
  "Tier_II_Hispanic_American",
  "Tier_II_Native_American",
  "Tier_II_Asian_Pacific_American",
  "Tier_II_Unknown_Minority",
  "Total_Tier_II_Minority",
  "Tier_II_Women",
  "Tier_II_LGBT",
  "Tier_II_Disabled_Veteran",
  "Tier_II_Less_Duplicated_Amount",
  "Combined_Tier_II_Total"
)

# Function to calculate correlations
calculate_correlations <- function(vars) {
  correlation_data <- final_hospitals %>%
    select(all_of(c(vars, "NET_INCOME"))) %>%
    filter(complete.cases(.))  # Remove rows with any NA values
  
  # Calculate the correlation matrix
  correlation_matrix <- cor(correlation_data, use = "complete.obs")
  
  # Extract correlations with NET_INCOME
  cor_with_net_income <- correlation_matrix["NET_INCOME", vars]
  
  return(cor_with_net_income)
}

tier1_correlations <- calculate_correlations(tier1_vars)
tier2_correlations <- calculate_correlations(tier2_vars)

# Create separate summary tables for the correlations

# Overall correlations table
overall_correlations <- data.frame(
  Variable = names(cor_with_net_income_overall),
  Correlation = cor_with_net_income_overall
)

# Tier 1 correlations table
tier1_correlations_table <- data.frame(
  Variable = names(tier1_correlations),
  Correlation = tier1_correlations
)

# Tier 2 correlations table
tier2_correlations_table <- data.frame(
  Variable = names(tier2_correlations),
  Correlation = tier2_correlations
)

# Function to format table output
format_table <- function(correlation_df, title) {
  cat(paste("\n###", title, "\n"))
  kable(correlation_df, col.names = c("Variable", "Correlation"), row.names = FALSE)
}

# Print the Overall Correlations table
format_table(overall_correlations, "Overall Correlations with NET_INCOME")

### Overall Correlations with NET_INCOME 
Variable Correlation
Total_African_American 0.1427578
Total_Hispanic_American 0.5110144
Total_Native_American -0.0488233
Total_Asian_Pacific_American 0.3952289
Total_Unknown_Minority 0.0244854
Total_Minority 0.2128842
Total_Women 0.1891206
Total_LGBT -0.0268189
Total_Disabled_Veteran 0.0988168
# Print the Tier 1 Correlations table
format_table(tier1_correlations_table, "Tier 1 Correlations with NET_INCOME")

### Tier 1 Correlations with NET_INCOME 
Variable Correlation
Tier_I_African_American 0.1537354
Tier_I_Hispanic_American 0.5990161
Tier_I_Native_American -0.0884723
Tier_I_Asian_Pacific_American 0.5934617
Tier_I_Unknown_Minority 0.0379230
Total_Tier_I_Minority 0.2774464
Tier_I_Women 0.5919702
Tier_I_LGBT -0.0218214
Tier_I_Disabled_Veteran 0.0615648
Tier_I_Less_Duplicated_Amount 0.2164891
Combined_Tier_I_Total 0.3362344
# Print the Tier 2 Correlations table
format_table(tier2_correlations_table, "Tier 2 Correlations with NET_INCOME")

### Tier 2 Correlations with NET_INCOME 
Variable Correlation
Tier_II_African_American 0.0051133
Tier_II_Hispanic_American 0.0496001
Tier_II_Native_American 0.0659351
Tier_II_Asian_Pacific_American 0.0504749
Tier_II_Unknown_Minority -0.0708019
Total_Tier_II_Minority -0.0247828
Tier_II_Women -0.0734673
Tier_II_LGBT -0.0838868
Tier_II_Disabled_Veteran 0.0213014
Tier_II_Less_Duplicated_Amount 0.0449488
Combined_Tier_II_Total -0.0498203

What are hospitals saying in their commitment to supplier diversity?

Each hospital is also required to report a supplier diversity statement to state their commitment to procuring from diverse suppliers. While not required, a majority of hospitals in this dataset reported a statement. To identify common themes, I highlighted the top 20 unique words and 3-word phrases to identify common themes in supplier diversity statements.

From this analysis suppliers have a strong commitment to procuring from diverse suppliers in order to support the business needs of the hospitals. They also aim to drive some sort of competition, possibly between suppliers to lower prices.

final_hospitals %>%
  unnest_tokens(word, Supplier_Diversity_Statement) %>%
   filter(!str_detect(word, "\\d")) %>% 
   anti_join(stop_words, by = c("word"))%>%
  count(word, sort=TRUE) %>%
  top_n(20, n) %>%
  ggplot(aes(x = n, y = fct_reorder(word, n))) +
  geom_col()

final_hospitals %>%
  unnest_ngrams(token, Supplier_Diversity_Statement, n = 3) %>%
  count(token, sort=TRUE) %>%
  top_n(20, n) %>%
  ggplot(aes(x = n, y = fct_reorder(token, n))) +
  geom_col()

Conclusion

Overall, preliminary data suggest a potential positive relationship between procurement from supplier diversity and increased income for hospitals. This remains a critical priority for hospitals and medical centers throughout California. Notably, some of the highest-earning hospitals are not allocating significant resources to diverse suppliers. Further analysis with greater statistical power is essential to clarify this relationship. It is important to address health equity gaps and combat systemic racism and injustice by actively investing in and procuring from minority-owned businesses.

A potential other area of analysis could also include removing/isolating the amount of money spent on suppliers that are the industry standard. For example, Epic Health Systems is one of the industry leaders in electronic medical record software applications and holds medical records of 78% of patients in the United States. Its CEO and founder is businesswoman Judidth Faulker who is was called “the most powerful woman in healthcare” by Forbes in 2013. n this dataset, as an industry leader, Epic Health Systems would still be categorized as a minority supplier due to Judith Faulkner’s ownership status. This presents an interesting dynamic, as Epic’s influence and widespread use could skew the analysis of diversity spend, highlighting funds allocated to suppliers who are industry giants. Examining the data specifically for minority suppliers who are not major players in their industries could provide valuable insights into how funds are distributed among smaller, potentially emerging minority-owned businesses and the unique challenges they face in achieving industry traction and growth.